text: import numpy as np
  
  from .utils import is_scalar
  
  
  def li_ma_significance(n_on, n_off, alpha=0.2):
      """
      Calculate the Li & Ma significance.
  
      Formula (17) doi.org/10.1086/161295
  
      This functions returns 0 significance when n_on < alpha * n_off
      instead of the negative sensitivities that would result from naively
      evaluating the formula.
  
      Parameters
      ----------
      n_on: integer or array like
          Number of events for the on observations
      n_off: integer of array like
          Number of events for the off observations
      alpha: float
          Ratio between the on region and the off region size or obstime.
  
      Returns
      -------
      s_lima: float or array
          The calculated significance
      """
  
      scalar = is_scalar(n_on)
  
      n_on = np.array(n_on, copy=False, ndmin=1)
      n_off = np.array(n_off, copy=False, ndmin=1)
  
      with np.errstate(divide="ignore", invalid="ignore"):
          p_on = n_on / (n_on + n_off)
          p_off = n_off / (n_on + n_off)
  
          t1 = n_on * np.log(((1 + alpha) / alpha) * p_on)
          t2 = n_off * np.log((1 + alpha) * p_off)
  
          # lim x+->0  (x log(x)) = 0
          t1[n_on == 0] = 0
          t2[n_off == 0] = 0
  
          ts = t1 + t2
  
          significance = np.sqrt(ts * 2)
  
      significance[n_on < (alpha * n_off)] = 0
  
      if scalar:
          return significance[0]
  
      return significance
  